Moneyball for bikes: Can we use data to win the transportation game?

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Here’s a new kind of Bikenomics — What if we could increase bicycle ridership not through general encouragement or infrastructure or culture change, but through strategic, targeted tweaks aimed at identifying potential bicycling demographics and tipping them? Or, to put it inversely, what if we could quantify barriers to bicycling and use that data to kick holes in them?

On the train on the way home from tour, we watched Moneyball. It’s a movie about the revolution in professional baseball management. The old way of picking players for teams was to try to choose the ideal player for each position; the Oakland As hired a Harvard economist who shifted their strategy to instead looking at the aggregate stats among all players to build up a team designed to, overall, produce the highest score. The theory was that a crappy first baseman that other teams didn’t want might still be a winning pick if he always got on base when he was at bat. It worked, and the As won a record 20 games in a row that season.

We got home to the long-awaited news that the brilliant Walkscore has expanded their realm to bicycling. BikeScore ranks every address in a city by its proximity to stores, schools, parks, workplaces, and transit — rated in terms of bicycling distance along bike-friendly roads. It’s an imperfect tool but from reports I’ve heard so far everyone seems to think it’s hitting home runs as far as their own neighborhoods go.

This all made me think about major demographic shifts we’ve seen in bicycling. For instance, when the folks who now run the shop Clever Cycles imported their first container of Dutch bakfiets cargo bikes to Portland in 2007, that set off the family bike revolution here. A lot of people were ready to ride, but they needed a way to carry their kids and stuff.

The bakfietsen were a heavy and imperfect solution for navigating Portland’s hills with your family, but they were all that were needed to start a huge trend that is continuing to boom. What if we could use numbers to find the next trend?

We would have to ask the right questions to get useful data, of course, and it strikes me that this has long been a sticking point. Portland’s newest bike advocacy group, Andando en Bicicletas en Cully (I wrote about it when it launched), has set an excellent example of this — a community survey found that 85% of people in the group’s neighborhood were interested in bicycling. The biggest barrier identified wasn’t lack of trails or clothing or showers at work or knowledge of safe riding or any of the reasons you usually get when you ask advocates — it was bike theft. As a result, the organization is raising money to build secure bike parking.

Will this result in a huge increase in ridership in Cully? We’ll see. Their survey is smart, but it’s still part of the old baseball game that we’re all playing. I absolutely respect this and the thousands of other smart and mostly effective efforts out there to reduce barriers to bicycling — but I would love to see us harness the power of regression analysis to do it even better.

For a good starting resource for bicycle numbers to crunch, check out the Bikenomics zine, available in print or for the Kindle.